Our laboratory has developed models of human disease in yeast that have uncovered specific cellular defects underlying hereditary and sporadic protein misfolding diseases. We wish to make use of these models for the identification of molecules with therapeutic potential. Cyclic peptides are a proven class of cellular effector that is largely unexplored as a source of molecular therapeutics. Recently, we have developed a novel high throughput method of screening libraries of small, genetically encoded cyclic peptides for molecules that prevent cell death in our yeast models. The genetic encoding of the cyclic peptides allows orders of magnitude more compounds to be screened faster, at lower cost, and with less need for mechanization than with existing procedures. We have applied this method to our model of Parkinson's disease, in which overexpressed human alpha-synuclein aggregates and causes toxicity in yeast. Two promising hits from a library of octamer cyclic peptides have resulted. In this proposal, we seek initially to identify more hits by constructing and screening libraries of cyclic peptides of several different ring sizes. We will characterize the molecular mechanisms of the hits that result by examining their effects on alpha-synuclein aggregation and the structure of cellular organelles, and by determining their protein targets. We will also test representatives of each class of hit in worm and rat models of Parkinson's disease, and begin to optimize their activity through SAR analysis. The rapid selection procedure made possible by genetically encoding cyclic peptides, and the inherent advantages of the cyclic peptide scaffold make this a powerful method for identifying new compounds that can lead to therapeutics for protein misfolding diseases. Parkinson's Disease is a devastating neurodegenerative disorder that affects more than 2% of Americans over the age of 65, making the need acute to accelerate ways of finding drugs to treat it. We have recreated much of the disease in yeast, a very small organism that grows much faster and is much more manipulable than any of the model systems now in use. These significant advantages will allow us to search through many more potential drugs and do it much faster than is now possible, as well as to examine types of potentially promising drugs that so far have received little attention.